new_crime_file

#  Comments by Ronald Washington III ----------------------------------------------------------------
# The code below was developing as a Senior Research project for DA 401 ("Seminar in Data Analytics")
# at Denison University. I was inspired to conduct research regarding my home-city Chicago, IL and 
# perform some investigatory analysis on the evolution of crime within the city from 2001-present day.
# The data utlized within my research was provided by the Chicago Police Department, and can be found
# at (https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2). This data includes
# information regarding important information such as date, type of crime, crime code, description, arrest 
# identification, police district, longitude and latitude, and etc. As I began my analysis for this project
# I gathered inspiration for my analysis from numerous other researcher that investigated crime in other 
# cities such as Baltimore, LA, New York, and San Francisco. (Each of these references will be listed below).
# From this investigation I intend to discover the distributions of various crimes occuring within Chicago, 
# and hopfully be able to map out each crime type. From there I wish to perform clustering and classification 
# methods to predict the types of crimes that can occur in the future. Furthermore, I am particularly interested
# in being able to recognize patterns of crime that occur in the city and potentially being able to identify 
# gang activtiy and its evolution in influencing Chicago Crime.
#
# STATE THE DATA SET USED TO IDENTIFY GANG ACTIVITY
#
# My primary objectives for the project were to (1) explore the data, (2) perform descriptive analysis and 
# various forms of clustering and classifying of crime, and (3) and ultimately visualization. 
#
# Ronald Washington III (GitHub: https://github.com/ron2297)

# File Descriptions -----------------------------------------------------------------------------------
#
# Crime Crime Data for 2001 through Present Day (2019) downloaded from Chicago Data Portal
# at (https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2) (n = 6823075)

Importing Libraries

Reading in Data

Pre-processing - Cleaning of Crime Data

Reducing the number of crime types/descriptions

Number of Different types of Crime type

Creating classifications for crime types

#write.csv(df_Crime, file = "processs_crime_data.csv")
df_Crime <- read.csv(("C:/Users/Bloody Dachi/Documents/Chicago_Crime_Evolution_Analysis/Analysis_Folder/processs_crime_data.csv"))

Removing Outliers from Longitude and Latitude

df_Crime=df_Crime %>% filter(Latitude > 40) 
df_Crime=df_Crime %>% filter(Longitude > -90) 

#WantedData=df_Crime[df_Crime$Latitude %in% coors$df_Crime.Latitude, ]
#WantedData=df_Crime[df_Crime$Longitude %in% coors$df_Crime.Longitude, ]
summary(df_Crime$Longitude)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -87.93  -87.71  -87.67  -87.67  -87.63  -87.52

Review of Simplified Crime Category Counts

df_Crime <- subset(df_Crime, !is.na(df_Crime$crime))
(table(df_Crime$crime)) #occurances of each

## 
##                             ARSON                           ASSAULT 
##                             11182                            422157 
##                           BATTERY                          BURGLARY 
##                           1239979                            389584 
## CONCEALED CARRY LICENSE VIOLATION                            DAMAGE 
##                               326                            774861 
##                 DOMESTIC VIOLENCE                              DRUG 
##                                 1                            705793 
##                             FRAUD                          HOMICIDE 
##                            258576                                41 
##                 HUMAN TRAFFICKING                     NON-VIOLATION 
##                                53                             99986 
##                    NON - CRIMINAL                             OTHER 
##                                38                            420596 
##                         RITUALISM                           ROBBERY 
##                                22                            256719 
##                            SEXUAL                             THEFT 
##                            118375                           1420555 
##                          TRESPASS                           VEHICLE 
##                            194377                            314005 
##                         VIOLATION 
##                            122284

Data Exploration

Aggregated Data Presenting Top 6 Types of Crimes and Relative Distribution

huh<- as.data.frame(df_Crime)
df_crime_daily <- huh %>%
  group_by(crime,Time.tag,DayOfWeek) %>%
  summarize(count = n()) %>%
  mutate(norm = count/sum(count)) %>%
  arrange(crime)

df_category <- sort(table(huh$crime),decreasing = TRUE)
df_category <- data.frame(df_category[df_category > 1])
colnames(df_category) <- c("Category", "Frequency")
df_category$Percentage <- df_category$Frequency / sum(df_category$Frequency)
x<-head(df_category)
x #gathers top 6

##   Category Frequency Percentage
## 1    THEFT   1420555 0.21046790
## 2  BATTERY   1239979 0.18371396
## 3   DAMAGE    774861 0.11480257
## 4     DRUG    705793 0.10456953
## 5  ASSAULT    422157 0.06254633
## 6    OTHER    420596 0.06231505

Visualization of Crimes and Arrest Relationship

df_Crime$crime <- as.factor(df_Crime$crime)

by_Date <- df_Crime %>% group_by(Date) %>% summarise(Total = n())
tseries <- xts(by_Date$Total, order.by=as.POSIXct(by_Date$Date))

#Arrests_by_Date$Date[!(Arrests_by_Date$Date %in% by_Date$Date)]
## Creating timeseries of arrests made
Arrests_by_Date <- (df_Crime[df_Crime$Arrest == 'true',]) %>% group_by(Date) %>% summarise(Total = n())
arrests_tseries <- xts(Arrests_by_Date$Total, order.by=as.POSIXct(Arrests_by_Date$Date))


hchart(tseries, name = "crime") %>% 
 hc_add_series(arrests_tseries, name = "Arrest") %>%
 hc_add_theme(hc_theme_monokai()) %>%
 hc_credits(enabled = TRUE, text = "Sources: City of Chicago Administration and the Chicago Police Department", style = list(fontSize = "12px")) %>%
 hc_title(text = "Trend of Chicago Crimes and Arrests") %>% 
 hc_legend(enabled = TRUE)

cat_value_freq <-  
  df_Crime %>% 
  select_if(is.factor) %>% 
  select_if(function(x) !is.ordered(x)) %>% 
  gather("var", "value") %>% 
  group_by(var) %>% 
  count(var, value) %>%
  mutate(prop = prop.table(n)) %>% 
  filter(prop > .02)

## Warning: attributes are not identical across measure variables;
## they will be dropped

cat_plot1 <-
  ggplot(data = cat_value_freq,
       aes(x = reorder(stringr::str_wrap(value, 20), prop),
           y = prop)) +
  geom_bar(stat = "identity", fill = "tomato3") +
  coord_flip() +
  facet_wrap(~var,ncol=4, scales = "free") +
  ggthemes::theme_fivethirtyeight()

cat_plot1

# %-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0
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# %-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0

Mapping Chicago

chicago = get_stamenmap(bbox = c(left = -88.0225, bottom = 41.5949, right = -87.2713, top = 42.0677), maptype = c("toner-lite"), zoom = 13)

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map = ggmap(chicago)
map

# #map <- get_map("Chicago", zoom = 12, color = "bw")
# ggmap(map, extent='device') + 
#      geom_point(data=sample_n(df_Crime,100000), aes(x=X.Coordinate, y=Y.Coordinate),
#                 alpha = 1/10,color = "red")+
#      scale_colour_brewer(type="qual")

chicago <- get_stamenmap(bbox = c(left = -88.0225, bottom = 41.5949, 
                                  right = -87.2713, top = 42.0677), 
                         zoom = 10)

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ggmap(chicago)

map_contours <- function(data_trunc, alp) {
        p1 = ggmap(chicago, extent='device') + 
        geom_point(data=data_trunc, aes(x=Longitude, y=Latitude), alpha= alp) + 
        stat_density2d(aes(x=Longitude, y=Latitude,
                           fill = ..level.., alpha = ..level..),
                size = 0.1, data = data_trunc, n=100,
                geom = "polygon") +
        theme(legend.position="none")
        return(p1)
}


plot_marginals <- function(data_trunc) {
        p2 = ggplot(data=data_trunc, aes(x=Longitude, y=Latitude), alpha=0.1)+
        geom_point()
        p2 = ggMarginal(p2 + theme_gray(), type = "histogram",
           fill = "steelblue", col = "darkblue")
        return(p2)
}

p2 = plot_marginals(sample_n(df_Crime,10000))
plot(p2)

p2 = map_contours(sample_n(df_Crime,10000),.1)
p2

Crime vs Years

data_plot = df_Crime %>%
        group_by(Year,Month) %>%
        summarise(count = n()) 
ggplot(data = data_plot,aes(group = Month, x=Year, y=count,col = Month)) + 
  geom_boxplot() + 
  ylab("Crime count in each month of a year")

# top10_df <- df_Crime[df_Crime$crime %in% x[,1],]
# head(top10_df)
# top10_df

ggmap(chicago, extent='device', legend="topleft") + 
  geom_point(data = df_Crime[df_Crime$crime %in% x[,1],], aes(x=Longitude, y=Latitude, col = crime), 
             size = 1.2, alpha = 0.5) + ggtitle("Mapping top 10 crimes")

bbox = c(-88.0225,41.5949,-87.2713,42.0677)
chicago_map <- get_map(location = bbox, source = "stamen", maptype = "toner-lite")

#df_Crime$
# plot<-ggmap(chicago_map)+
#   geom_point(data = df_Crime,aes(x=Longitude,y=Latitude), color = "green", size = 0.5, alpha = 0.01)+theme_light()+labs(title = "ass")
# plot

Top 10 is actually Top 6

map_crime <- function(crime_df) {
  #filtered <- filter(crime_df, Primary.Type %in% crime)
  plot <- ggmap(chicago_map, extent='device') + 
          geom_density2d(data = crime_df%>% filter(Primary.Type %in% df_Crime[df_Crime$crime %in% x[,1],]$Primary.Type ) , 
          aes(x = Longitude, y = Latitude), size = 0.3) + stat_density2d(data = crime_df%>% filter(Primary.Type %in% df_Crime[df_Crime$crime %in% x[,1],]$Primary.Type ) , 
          aes(x = Longitude, y = Latitude, fill = ..level.., alpha = ..level..), size = 0.01,
          bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red", 
          guide = FALSE) + scale_alpha(range = c(0, 0.3), guide = FALSE)+
            facet_wrap_paginate(~ crime, ncol = 3,page =1)
  return(plot)
}
#length(unique(WantedData$crime))
# Display Top 6 Crime Types Desnity Distribution across Chicago
map_crime(df_Crime[1:1000000,])